cbaziotis · October 22, 2024 08:31 · DeliaX · Sep 20, 2017 · DeliaX · Sep 20, 2017
diff --git a/Attention.py b/Attention.py

 from keras import backend as K, initializers, regularizers, constraints
 from keras.engine.topology import Layer


 def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':
        # todo: check that this is correct
        return K.squeeze(K.dot(x, K.expand_dims(kernel)), axis=-1)
    else:
        return K.dot(x, kernel)


 class Attention(Layer):
    def __init__(self,
                 W_regularizer=None, b_regularizer=None,
                 W_constraint=None, b_constraint=None,
                 bias=True,
                 return_attention=False,
                 **kwargs):
        """
        Keras Layer that implements an Attention mechanism for temporal data.
        Supports Masking.
        Follows the work of Raffel et al. [https://arxiv.org/abs/1512.08756]
        # Input shape
            3D tensor with shape: `(samples, steps, features)`.
        # Output shape
            2D tensor with shape: `(samples, features)`.
        :param kwargs:
        Just put it on top of an RNN Layer (GRU/LSTM/SimpleRNN) with return_sequences=True.
        The dimensions are inferred based on the output shape of the RNN.


        Note: The layer has been tested with Keras 1.x

        Example:
        
            # 1
            model.add(LSTM(64, return_sequences=True))
            model.add(Attention())
            # next add a Dense layer (for classification/regression) or whatever...

            # 2 - Get the attention scores
            hidden = LSTM(64, return_sequences=True)(words)
            sentence, word_scores = Attention(return_attention=True)(hidden)

        """
        self.supports_masking = True
        self.return_attention = return_attention
        self.init = initializers.get('glorot_uniform')

        self.W_regularizer = regularizers.get(W_regularizer)
        self.b_regularizer = regularizers.get(b_regularizer)

        self.W_constraint = constraints.get(W_constraint)
        self.b_constraint = constraints.get(b_constraint)

        self.bias = bias
        super(Attention, self).__init__(**kwargs)

    def build(self, input_shape):
        assert len(input_shape) == 3

        self.W = self.add_weight((input_shape[-1],),
                                 initializer=self.init,
                                 name='{}_W'.format(self.name),
                                 regularizer=self.W_regularizer,
                                 constraint=self.W_constraint)
        if self.bias:
            self.b = self.add_weight((input_shape[1],),
                                     initializer='zero',
                                     name='{}_b'.format(self.name),
                                     regularizer=self.b_regularizer,
                                     constraint=self.b_constraint)
        else:
            self.b = None

        self.built = True

    def compute_mask(self, input, input_mask=None):
        # do not pass the mask to the next layers
        return None

    def call(self, x, mask=None):
        eij = dot_product(x, self.W)

        if self.bias:
            eij += self.b

        eij = K.tanh(eij)

        a = K.exp(eij)

        # apply mask after the exp. will be re-normalized next
        if mask is not None:
            # Cast the mask to floatX to avoid float64 upcasting in theano
            a *= K.cast(mask, K.floatx())

        # in some cases especially in the early stages of training the sum may be almost zero
        # and this results in NaN's. A workaround is to add a very small positive number ε to the sum.
        # a /= K.cast(K.sum(a, axis=1, keepdims=True), K.floatx())
        a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())

        weighted_input = x * K.expand_dims(a)

        result = K.sum(weighted_input, axis=1)

        if self.return_attention:
            return [result, a]
        return result

    def compute_output_shape(self, input_shape):
        if self.return_attention:
            return [(input_shape[0], input_shape[-1]),
                    (input_shape[0], input_shape[1])]
        else:
            return input_shape[0], input_shape[-1]

	from keras import backend as K, initializers, regularizers, constraints
	from keras.engine.topology import Layer


	def dot_product(x, kernel):
	"""
	Wrapper for dot product operation, in order to be compatible with both
	Theano and Tensorflow
	Args:
	x (): input
	kernel (): weights
	Returns:
	"""
	if K.backend() == 'tensorflow':
	# todo: check that this is correct
	return K.squeeze(K.dot(x, K.expand_dims(kernel)), axis=-1)
	else:
	return K.dot(x, kernel)


	class Attention(Layer):
	def __init__(self,
	W_regularizer=None, b_regularizer=None,
	W_constraint=None, b_constraint=None,
	bias=True,
	return_attention=False,
	**kwargs):
	"""
	Keras Layer that implements an Attention mechanism for temporal data.
	Supports Masking.
	Follows the work of Raffel et al. [https://arxiv.org/abs/1512.08756]
	# Input shape
	3D tensor with shape: `(samples, steps, features)`.
	# Output shape
	2D tensor with shape: `(samples, features)`.
	:param kwargs:
	Just put it on top of an RNN Layer (GRU/LSTM/SimpleRNN) with return_sequences=True.
	The dimensions are inferred based on the output shape of the RNN.


	Note: The layer has been tested with Keras 1.x

	Example:

	# 1
	model.add(LSTM(64, return_sequences=True))
	model.add(Attention())
	# next add a Dense layer (for classification/regression) or whatever...

	# 2 - Get the attention scores
	hidden = LSTM(64, return_sequences=True)(words)
	sentence, word_scores = Attention(return_attention=True)(hidden)

	"""
	self.supports_masking = True
	self.return_attention = return_attention
	self.init = initializers.get('glorot_uniform')

	self.W_regularizer = regularizers.get(W_regularizer)
	self.b_regularizer = regularizers.get(b_regularizer)

	self.W_constraint = constraints.get(W_constraint)
	self.b_constraint = constraints.get(b_constraint)

	self.bias = bias
	super(Attention, self).__init__(**kwargs)

	def build(self, input_shape):
	assert len(input_shape) == 3

	self.W = self.add_weight((input_shape[-1],),
	initializer=self.init,
	name='{}_W'.format(self.name),
	regularizer=self.W_regularizer,
	constraint=self.W_constraint)
	if self.bias:
	self.b = self.add_weight((input_shape[1],),
	initializer='zero',
	name='{}_b'.format(self.name),
	regularizer=self.b_regularizer,
	constraint=self.b_constraint)
	else:
	self.b = None

	self.built = True

	def compute_mask(self, input, input_mask=None):
	# do not pass the mask to the next layers
	return None

	def call(self, x, mask=None):
	eij = dot_product(x, self.W)

	if self.bias:
	eij += self.b

	eij = K.tanh(eij)

	a = K.exp(eij)

	# apply mask after the exp. will be re-normalized next
	if mask is not None:
	# Cast the mask to floatX to avoid float64 upcasting in theano
	a *= K.cast(mask, K.floatx())

	# in some cases especially in the early stages of training the sum may be almost zero
	# and this results in NaN's. A workaround is to add a very small positive number ε to the sum.
	# a /= K.cast(K.sum(a, axis=1, keepdims=True), K.floatx())
	a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())

	weighted_input = x * K.expand_dims(a)

	result = K.sum(weighted_input, axis=1)

	if self.return_attention:
	return [result, a]
	return result

	def compute_output_shape(self, input_shape):
	if self.return_attention:
	return [(input_shape[0], input_shape[-1]),
	(input_shape[0], input_shape[1])]
	else:
	return input_shape[0], input_shape[-1]